Talking to Computers: The Email

Talking to Computers: The Email is a monthly publication exploring the intersection of natural language technologies and product development, with a focus on building practical applications. It covers the challenges and strategies in utilizing large language models (LLMs), voice interfaces, and AI-driven product innovations, alongside considerations of usability, security, and market adoption.

Natural Language Technologies Large Language Models (LLMs) Voice Interface Usability AI in Product Development Data Privacy and Security User Interface Design Machine Learning-driven Search Prompt Engineering Market Trends in AI

The hottest Substack posts of Talking to Computers: The Email

And their main takeaways

Haystack 2024 - My Favorite Talks

1 HN point • 09 Jul 24
🕹 Technology Information Retrieval Artificial Intelligence Machine Learning Data Analysis
  1. Retrieval Augmented Generation (RAG) is a hot topic this year, mixing search and text generation. It's being used in new and complex ways, even integrating images and tables.
  2. Vector and hybrid searches are also popular, combining traditional keyword searches with modern techniques for better results. This approach helps tailor searches more effectively.
  3. There were talks on various other topics, highlighting the importance of basics in search technology. Simple methods can still be very effective, especially for organizations trying to improve their search results.

Why Voice Failed as a Platform

2 HN points • 14 Sep 23
  1. Voice failed as a platform due to usability and discoverability issues.
  2. Adoption and privacy concerns did not cause voice to fail as a platform.
  3. Lack of a 'killer app' and business adoption hindered voice from becoming a successful platform.

June 22, 2023

0 implied HN points • 22 Jun 23
  1. Knowledge retrieval architecture for LLMs is crucial for connecting data sources to models for improved functionality.
  2. OpenAI's introduction of functions simplifies the process of passing arguments to described functions in models.
  3. Security and privacy certifications can be a significant competitive advantage, especially with increasing concerns over the use of LLMs in various sectors.

May 25, 2023

0 implied HN points • 25 May 23
  1. Chat-based interfaces can lack guidance and context for users, affecting usability.
  2. Prompt injection and instruction leakage are important concepts to consider in interface design.
  3. Evaluating startups utilizing LLMs should focus on providing unique value rather than just being a thin wrapper around the technology.

May 11th, 2023

0 implied HN points • 11 May 23
  1. Focus on keeping up with the latest in conversational and natural language technologies by subscribing to newsletters like Talking to Computers
  2. Stay updated on advancements in prompt engineering from online courses and competition collaborations
  3. Be aware of the rapid improvements in language models, the importance of being cutting edge, and the value of flexibility when it comes to AI innovations
Get a weekly roundup of the best Substack posts, by hacker news affinity:

First one, coming soon

0 implied HN points • 07 Aug 18
  1. First newsletter coming soon
  2. Author is Dustin Coates
  3. Check out Talking to Computers: The Blog

August 17, 2023

0 implied HN points • 17 Aug 23
  1. Measuring the impact and quality of ML-driven search is crucial for enhancing search experiences and evaluating search engines performance
  2. ML changes how we analyze the quality of search by focusing on context rather than keyword matching, making evaluations more challenging
  3. Human evaluations and testing in production are essential methods to ensure search engines are delivering relevant results to users

July 6, 2023

0 implied HN points • 06 Jul 23
  1. AI is becoming so powerful that it raises concerns about its impact on humanity.
  2. The concept of prompt engineering in AI is evolving towards problem formulation.
  3. Generative AI is showing potential benefits for workers with less experience.

June 8, 2023

0 implied HN points • 08 Jun 23
  1. Building products with LLMs can be challenging due to issues like getting enough information, prompt engineering, and correctness versus usefulness.
  2. Andrej Karpathy provides easy-to-follow insights on the state of GPT through his informative videos.
  3. OpenAI offers best practices for working with GPTs, emphasizing clear instructions, reference text, and systematic testing.

It's Tax Day: How Does the IRS Search Stack Up?

0 implied HN points • 15 Apr 24
🇺🇸 U.S. Politics Government Policy Taxation Public Services Bureaucracy
  1. The IRS search engine is not very helpful, especially when handling typos or poorly formed queries. It's important for a tax-related search engine to understand common mistakes.
  2. While the search bar on the IRS website is appropriately placed, it lacks features like search suggestions and autocomplete that could make finding answers easier.
  3. The search results can sometimes highlight useful information, but overall the IRS search system needs significant improvements to better serve the public.

A simple suggestion to improve search UX

0 implied HN points • 25 Mar 24
🕹 Technology User Experience Search E-commerce User Interaction
  1. Search suggestions help users type less and avoid mistakes, which is especially important on mobile devices. Fewer keystrokes lead to faster and more successful searches.
  2. Contextual suggestions can guide users to better queries, showing them related products or searches when they type. This can help them find what they actually want more easily.
  3. E-commerce websites, especially for groceries, benefit from these smart suggestions to help shoppers buy more items at once. It’s a unique approach that supports the way people shop for multiple products.

Handling Typos to Increase Speed to Success

0 implied HN points • 29 May 24
🕹 Technology Search Engines User Experience Data processing Software Development Information Retrieval
  1. Handling typos in search helps users find what they want faster, even if they misspell words. It makes the search experience easier for people who are not perfect spellers.
  2. Search engines use techniques like Levenshtein distance to manage typos, so they rank search results based on how closely they match users' misspelled queries.
  3. Contextual typo tolerance improves search results by considering the meaning behind the words, which is often missing in smaller e-commerce sites. This way, users get more relevant suggestions rather than just similar-looking words.

What the Humane AI Pin Launch Can Teach Product Builders

0 implied HN points • 30 Apr 24
🕹 Technology AI Product Design Wearables Natural Language Market Strategy
  1. When creating a new product, focus on doing one thing really well. This way, you can set realistic expectations and deliver a better experience.
  2. Natural language products come with unique challenges, like errors in speech recognition and resource demands. It's best to narrow your focus to avoid these problems.
  3. Building a small, specialized product can be more effective than trying to make something for everyone. Starting small allows for improvement and expansion later.

Three lines that define search

0 implied HN points • 18 Mar 24
🕹 Technology Search User Experience Information Retrieval Data Management Machine Learning
  1. Users often want to find information with the least amount of actions. A well-designed interface can let them get what they need in just one action, like typing a query.
  2. The difference between finding and discovery is important. Finding is when users know what they want and search for it, while discovery is about stumbling upon things they didn't even know they wanted.
  3. Precision and recall are two key ideas in search results. Precision means showing only the most relevant results, while recall means showing all relevant results, even if some are less relevant.

My experience building an app entirely via an LLM

0 implied HN points • 14 Aug 24
🕹 Technology AI Tools Software Development User Experience Iterative design App Development Code Quality
  1. Using AI tools like Claude can speed up app development, especially for small coding tasks. But, it's not perfect and sometimes leads to unexpected issues.
  2. Designing the app can be tough, as AI might not help much with styling. You might end up doing more work to fix design flaws after the initial code is generated.
  3. Even when using an AI, having some coding knowledge is important. You still need to understand what changes to make and how to fix problems that come up.

Speed to Search Success: Synonyms

0 implied HN points • 14 Jun 24
🕹 Technology Data Analysis User Behavior Information Retrieval Algorithm Design
  1. Using synonyms in search helps users find what they need faster. It allows them to use their own words instead of worrying about exact terms.
  2. Creating synonyms can be tricky, but observing how users search can help build a better list. Watching what terms people actually use is more effective than guessing.
  3. While synonyms cover many cases, they struggle with specific long terms. For more complex searches, vector search technology might be a better solution.

Why speed should be your first priority

0 implied HN points • 15 May 24
🕹 Technology Internet Business E-commerce User Experience Performance
  1. Prioritize speeding up processes to save users time. When making choices, consider what helps users get what they need faster.
  2. Saving time is beneficial for businesses, like e-commerce and streaming services, as it leads to more sales and viewings.
  3. Look at projects through the lens of speed and efficiency. Evaluate how your features help users save time and adjust priorities accordingly.

Book Review: AI-Powered Search

0 implied HN points • 08 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Information Retrieval User Experience
  1. AI is changing how search works, moving towards using machine learning to improve results based on user feedback and interactions. This means less manual work and more personalized, efficient searches.
  2. Natural language processing helps search engines understand context and synonyms, making it easier to find relevant information. Understanding language structure allows for better handling of queries.
  3. Learning to rank is a powerful tool for improving search results based on user behavior, but it needs quality data to be effective. Without the right data, the improvements may not be as impactful as expected.

The Optimal Amount of Irrelevant Search Results is Non-Zero

0 implied HN points • 22 Apr 24
🕹 Technology Search Engines E-commerce Artificial Intelligence User Experience Data Analysis
  1. Sometimes, it's okay to have a few irrelevant search results mixed in with the good ones. This balance can help show more options, even if some aren't what you wanted.
  2. Businesses often choose to include a small number of unrelated items in search results. This helps them find a middle ground between showing only perfect matches and potentially missing out on useful items.
  3. In systems like AI, having occasional mistakes or 'hallucinations' can spark creativity. It's about finding the right balance that works for the situation.

The Three Types of LLM-Powered Apps

0 implied HN points • 02 Nov 23
  1. LLM-powered apps can be categorized into three types: LLM plus prompt, LLM plus data, LLM plus product.
  2. LLM plus prompt apps are the simplest to build but are less defensible compared to the other types.
  3. LLM plus product apps are the most useful and defensible as they are deeply integrated into an existing product.

Using Redirects for Broad Search Terms

0 implied HN points • 04 Mar 24
💼 Business Retail E-commerce Marketing SEO User Experience
  1. Redirects can help handle broad search queries better by sending users to optimized category pages instead of generic search results. This helps manage the overwhelming number of options users face.
  2. Using a smart layout with options for refining searches can improve user experience. It helps guide shoppers to what they're truly looking for rather than just presenting them with endless choices.
  3. A hybrid approach that combines category redirects with engaging banners might be more effective. This way, it keeps essential information visible and caters to user needs without overwhelming them.

August 3, 2023

0 implied HN points • 03 Aug 23
  1. Building with Large Language Models (LLMs) presents challenges in dataset quality, tokenization, and prompt engineering.
  2. The ChatGPT user base is declining, with users complaining about 'dumber' answers but potentially due to increased usage revealing flaws.
  3. There are various security risks associated with using LLMs in companies, especially with external, user-facing integrations.